Field of the Invention
The invention relates to an optical fibre sensor system and a method for determining a location of a disturbance.
Introduction to the Invention
Oil, water, gas and other product pipelines form a critical network in every part of the world and are an easy target for intruders. The pipelines are also susceptible to earthquakes, tsunamis and to other geohazard incidents. Monitoring the pipelines in order to keep the pipelines safe from damage is a major challenge. The long distances, often through remote and hostile territory, make the costs of most conventional monitoring systems prohibitive. If an oil or gas pipeline is damaged it can have devastating impacts on human life and health due to explosions, fire and contamination; environment due to poisoning of flora and fauna, as well as the associated financial losses and damage to both image and reputation.
The pipelines are susceptible to various types of third-party interference, such as deliberate acts (illegal tapping, sabotage) or unintended disruption (construction work, farming). This third-party interference can cause huge financial and environmental damage and loss of reputation to the pipeline operators. A reliable, real-time pipeline monitoring system is therefore required in order to detect events of interest and thereby protect nature, human health and economic interests.
According to a survey in 2009, about 36% of all worldwide pipeline leaks were caused by third party interference (TPI), such as illegal tapping, sabotage or construction work. Examples of the events of interest due to third party interference include: 4 Oct. 2001 Fairbanks Ak. (USA), 990 t oil due to sabotage; 2000 Tschernigow, Ukraine, loss of 500,000 liters diesel due to illegal tapping; 10 Jun. 1999 Bellingham, Whatcorn Creek, Wash. (USA), 880 t petrol due to construction work, financial damage USD 45 million.
Illegal tapping is a major problem in emerging countries like India, China and South America. In 2011 for example, Petróleos Mexicanos (PEMEX) counted 1,324 cases of illegal tapping in Mexico. Every day Pemex estimates a loss of 40,000 liters of oil and gas, which sums up to an annual damage of more than 1 billion US$ of loss. Such damage could be avoided or substantially reduced, if third party interference was detected before or close to the occurrence of the interference.
Many times the pipeline monitoring is done by walking, driving and flying along the pipeline. The annual costs for walking and driving the line vary from 100 to 350 per kilometer. The additional costs for flying the line amount to 4.50 per kilometer. Usually two inspections are performed per month, which adds up to 108 per kilometer (4.50/km×2 flights/month×12 months). The annual costs are between 208 and 458 per kilometer.
Description of the Related Art
A number of systems for the sensing of an acoustic disturbance are known. The acoustic disturbance can be representative of damage to the pipelines through third party interference or geohazards, as described in the introduction, or potential damage due to approaching vehicles or individuals intending to damage the pipeline. These systems involve the use of an optical fibre laid alongside the pipeline, which acts as a sensor and detects changes in the pattern of back-scattered radiation in order to sense an acoustic disturbance. For example, Pimon GmbH, Munich, Germany, sells an apparatus PMS2500-vibrO that utilizes distributed fibre optical sensing technology to detect the acoustic disturbance. The PMS2500-vibrO system combines an optical time domain reflectometer (OTDR) with an analysis and pattern recognition software and offers a customized interface with geographic information system (GIS) mapping.
International Patent Applications No WO 2011/05813, WO 2011/015812 and WO 2011/059501 (QinetiQ) all teach various aspects of using a distributed fibre optic sensing system for establishing events of interest. Similarly UK Patent Application No GB 2 491 658 also teaches a method and system for locating an acoustic disturbance. These patent applications all have in common that the systems analyse the back-scattered radiation from the optical fibre to establish the event of interest. Such systems are useful in determining an event of interest from the acoustic disturbance, but the systems are known to produce “false positives” in which events are identified that are of no interest and fail to identify some events of interest, in particular when such events of interest have not been seen before.
One solution to the issue of incorrect identification of events would be to use an artificial neural network (ANN) to train the system to recognise the event of interest. The ANNs are computational models and are inspired by animal central nervous systems, in particular the brain, that are capable of machine learning and pattern recognition. The ANNs are usually presented as a system of nodes or “neurons” connected by “synapses” that can compute values from inputs, by feeding information from the inputs through the ANN. The synapses are the mechanism by which one of the neurons passes a signal to another one of the neurons.
One example of the A is for the recognition of handwriting. A set of input neurons may be activated by pixels in a camera of an input image representing a letter or a digit. The activations of these input neurons are then passed on, weighted and transformed by some function determined by a designer of the ANN to other neurons, etc. until finally an output neuron is activated that determines which character (letter or digit) was imaged. ANNs have been used to solve a wide variety of tasks that are hard to solve using ordinary rule-based programming, including computer vision and speech recognition.
There is no single formal definition of an ANN. Commonly a class of statistical models will be termed “neural” if the class consists of sets of adaptive weights (numerical parameters that are tuned by a learning algorithm) and are capable of approximating non-linear functions of the inputs of the statistical models. The adaptive weights can be thought of as the strength of the connections (synapses) between the neurons.
The ANNs have to be trained in order to produce understandable results. There are three major learning paradigms: supervised learning, unsupervised learning and reinforcement learning.
In a supervised learning, the learning paradigms all have in common that a set of pre-analysed data, for example a waveform, is analysed by the ANN and the weights of the connections (synapses) between the neurons in the ANN are adapted such that the output of the ANN is correlated with a known event. There is a cost involved in this training. An improvement in the efficiency of the results of the ANN can be obtained by using a greater number of data items representing the known event in a training set. The greater number of data items requires, however, an increase in computational power and time for the analysis in order to get the correct results. There is therefore a trade-off that needs to be established between the time taken to train the A and the accuracy of the results.
Recent developments in the ANNs involve so-called ‘deep learning’. Deep learning is a set of algorithms that attempt to use layered models of inputs. Jeffrey Heaton, University of Toronto, has discussed deep learning in a review article entitled ‘Learning Multiple Layers of Representation’ published in Trends in Cognitive Sciences, vol. 11, No. 10, pages 428 to 434, 2007. This publication describes multi-layer neural networks that contain top-down connections and training of the multilayer neural networks one layer at a time to generate sensory data, rather than merely classifying the data.
Neuron activity in the prior art ANNs is computed for a series of discrete time steps and not by using a continuous parameter. The activity level of the neuron is usually defined by a so-called “activity value”, which is set to be either 0 or 1, and which describes an ‘action potential’ at a time step t. The connections between the neurons, i.e. the synapses, are weighted with a weighting coefficient, which is usually chosen have a value in the interval [−1.0, +1.0]. Negative values of the weighting coefficient represent “inhibitory synapses” and positive values of the weighting coefficient indicate “excitatory values”. The computation of the activity value in the ANNs uses a simple linear summation model in which weighted ones of some or all of the active inputs received on the synapses at a neuron are compared with a (fixed) threshold value of the neuron. If the summation results in a value that is greater than the threshold value, the following neuron is activated.
One example of a learning system is described in international patent application No. WO 1998 027 511 (Geiger), which teaches a method of detecting image characteristics, irrespective of size or position. The method involves using several signal-generating devices, whose outputs represent image information in the form of characteristics evaluated using non-linear combination functions.
International patent application No. WO 2003 017252 relates to a method for recognizing a phonetic sound sequence or character sequence. The phonetic sound sequence or character sequence is initially fed to the neural network and a sequence of characteristics is formed from the phonetic sequence or the character sequence by taking into consideration stored phonetic and/or lexical information, which is based on a character string sequence. The device recognizes the phonetic and the character sequences by using a large knowledge store having been previously programmed.
An article by Hans Geiger and Thomas Waschulzik entitled ‘Theorie and Anwendung strukturierte konnektionistische Systeme’, published in Informatik-Fachreichte, Springer-Verlag, 1990, pages 143-152 also describes an implementation of a neural network. The neurons in the ANN of this article have activity values between zero and 255. The activity values of each one of the neurons changes with time such that, even if the inputs to the neuron remain unchanged. The output activity value of the neuron would change over time. This article teaches the concept that the activity value of any one of the nodes is dependent at least partly on the results of earlier activities. The article also includes brief details of the ways in which system may be developed.
The ANNs of the prior art need to be trained using patterns indicative of events of interest. They may then be good at recognising such “known” events of interest, but can fail if the event is own which will lead to a pattern of back-scattered radiation in the fibre optic that is unknown. The ANNs also rely on the events of interest being correctly programmed and identified by an expert and also that the selection of weighted factors to determine the event of interest is also known.
One further type of event that is difficult to detect with current systems is an event that is best identified from a sequence of events. Suppose, for example, that a group of individuals wish to tap an oil pipeline in order to take some of the oil. The sequence of events will probably involve an excavator approaching the pipeline and possible going backwards and forwards over the pipeline before stopping and putting out its supporting feet to stabilise the excavator. The excavator begins to excavate a large hole near the pipeline followed by manual digging by a group of, for example, three to five people near to the pipeline. Subsequently, the excavator will be driven off and a valve placed in the pipeline to remove the oil. A tanker arrives to remove the tapped oil. Each of these individual events may in themselves not be of concern (although the manufal digging near the pipeline may be indicative of an attempt to steal oil). However, the entire sequence of events will be highly relevant. There is therefore a need for a system that is able to combine the signals from all of the events in order to identify the attempt to steal the oil.
In some instances, the valve is left in place and re-used to tap the oil. This will lead to a different set of detected events. If the location can be correlated with a location in which an unusual set of events had previously been detected, then there is a need to provide an urgent warning that a further attempt at extracting oil is being made.